Bottom Line:
Salmonella is a primary cause of enteric diseases in a variety of animals.Application of high-throughput analyses have generated large amounts of data and necessitated the development of computational approaches for data integration.Thus, reconstructing the global regulatory network during infection or, at the very least, under conditions that mimic the host cellular environment not only provides a bird's eye view of Salmonella survival strategy in response to hostile host environments but also serves as an efficient means to identify novel virulence factors that are essential for Salmonella to accomplish systemic infection in the host.

ABSTRACTSalmonella is a primary cause of enteric diseases in a variety of animals. During its evolution into a pathogenic bacterium, Salmonella acquired an elaborate regulatory network that responds to multiple environmental stimuli within host animals and integrates them resulting in fine regulation of the virulence program. The coordinated action by this regulatory network involves numerous virulence regulators, necessitating genome-wide profiling analysis to assess and combine efforts from multiple regulons. In this review we discuss recent high-throughput analytic approaches used to understand the regulatory network of Salmonella that controls virulence processes. Application of high-throughput analyses have generated large amounts of data and necessitated the development of computational approaches for data integration. Therefore, we also cover computer-aided network analyses to infer regulatory networks, and demonstrate how genome-scale data can be used to construct regulatory and metabolic systems models of Salmonella pathogenesis. Genes that are coordinately controlled by multiple virulence regulators under infectious conditions are more likely to be important for pathogenesis. Thus, reconstructing the global regulatory network during infection or, at the very least, under conditions that mimic the host cellular environment not only provides a bird's eye view of Salmonella survival strategy in response to hostile host environments but also serves as an efficient means to identify novel virulence factors that are essential for Salmonella to accomplish systemic infection in the host.

Mentions:
A variety of approaches to modeling transcriptional regulation have been developed over the years, many of which are reviewed by Karlebach and Shamir (2008). When it comes to analyzing transcriptional output or developing predictive integrated models of regulation and metabolism on the genome-scale, constraint-based approaches such as network component analysis (NCA; Liao et al., 2003) and Boolean networks (Kauffman, 1969; Covert et al., 2001, 2008; Gianchandani et al., 2006; Klamt et al., 2006; Shlomi et al., 2007; Graudenzi et al., 2011) and more recently probabilistic models (Chandrasekaran and Price, 2010) have been used. Figure 3 provides an overview of these methods. These methods all require a regulatory network structure as an input that may be assembled from manual curation of the literature (Gama-Castro et al., 2008), ChIP studies (Cho et al., 2009), or inference methods (Sabatti et al., 2002, 2005; Gardner et al., 2003; Margolin et al., 2006; Faith et al., 2007) discussed above and in a variety of other reviews (Chou and Voit, 2009; De Smet and Marchal, 2010). The regulatory network structure is often referred to as the connectivity matrix as it is modeled as a matrix where all of the TFs are on one axis, all of the genes on the other axis, and a non-zero element in the matrix indicates that a TF is known to influence the expression of the corresponding gene. A second common feature for these methods is that they were designed to only approximate the interactions between a TF and its target genes: NCA employs a Hill-type equation for each TF-gene interaction, Boolean models represent each gene as on or off based on the sum of Boolean interactions for all TFs that interact with the gene, and probabilistic regulation of metabolism (PROM) uses linear weights derived from prior transcriptome data to constrain flux through a gene's associated enzymatic activities based on current TF expression profiles.

Mentions:
A variety of approaches to modeling transcriptional regulation have been developed over the years, many of which are reviewed by Karlebach and Shamir (2008). When it comes to analyzing transcriptional output or developing predictive integrated models of regulation and metabolism on the genome-scale, constraint-based approaches such as network component analysis (NCA; Liao et al., 2003) and Boolean networks (Kauffman, 1969; Covert et al., 2001, 2008; Gianchandani et al., 2006; Klamt et al., 2006; Shlomi et al., 2007; Graudenzi et al., 2011) and more recently probabilistic models (Chandrasekaran and Price, 2010) have been used. Figure 3 provides an overview of these methods. These methods all require a regulatory network structure as an input that may be assembled from manual curation of the literature (Gama-Castro et al., 2008), ChIP studies (Cho et al., 2009), or inference methods (Sabatti et al., 2002, 2005; Gardner et al., 2003; Margolin et al., 2006; Faith et al., 2007) discussed above and in a variety of other reviews (Chou and Voit, 2009; De Smet and Marchal, 2010). The regulatory network structure is often referred to as the connectivity matrix as it is modeled as a matrix where all of the TFs are on one axis, all of the genes on the other axis, and a non-zero element in the matrix indicates that a TF is known to influence the expression of the corresponding gene. A second common feature for these methods is that they were designed to only approximate the interactions between a TF and its target genes: NCA employs a Hill-type equation for each TF-gene interaction, Boolean models represent each gene as on or off based on the sum of Boolean interactions for all TFs that interact with the gene, and probabilistic regulation of metabolism (PROM) uses linear weights derived from prior transcriptome data to constrain flux through a gene's associated enzymatic activities based on current TF expression profiles.

Bottom Line:
Salmonella is a primary cause of enteric diseases in a variety of animals.Application of high-throughput analyses have generated large amounts of data and necessitated the development of computational approaches for data integration.Thus, reconstructing the global regulatory network during infection or, at the very least, under conditions that mimic the host cellular environment not only provides a bird's eye view of Salmonella survival strategy in response to hostile host environments but also serves as an efficient means to identify novel virulence factors that are essential for Salmonella to accomplish systemic infection in the host.

ABSTRACTSalmonella is a primary cause of enteric diseases in a variety of animals. During its evolution into a pathogenic bacterium, Salmonella acquired an elaborate regulatory network that responds to multiple environmental stimuli within host animals and integrates them resulting in fine regulation of the virulence program. The coordinated action by this regulatory network involves numerous virulence regulators, necessitating genome-wide profiling analysis to assess and combine efforts from multiple regulons. In this review we discuss recent high-throughput analytic approaches used to understand the regulatory network of Salmonella that controls virulence processes. Application of high-throughput analyses have generated large amounts of data and necessitated the development of computational approaches for data integration. Therefore, we also cover computer-aided network analyses to infer regulatory networks, and demonstrate how genome-scale data can be used to construct regulatory and metabolic systems models of Salmonella pathogenesis. Genes that are coordinately controlled by multiple virulence regulators under infectious conditions are more likely to be important for pathogenesis. Thus, reconstructing the global regulatory network during infection or, at the very least, under conditions that mimic the host cellular environment not only provides a bird's eye view of Salmonella survival strategy in response to hostile host environments but also serves as an efficient means to identify novel virulence factors that are essential for Salmonella to accomplish systemic infection in the host.